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https://github.com/chanzuckerberg/cellxgene.git
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bmccandless
8180be83b8
* Introduce a config file to cellxgene The config file format is in yaml. The default config is located in server/common/default_config.py. A user may create a yaml file that contains a subset of these fields. It can be used during cellxgene launch, or for hosted cellxgene. The code has also been refactored. Much of the logic to check arguments has moved from launch to app config. It is now possible to set the tiledb context parameters using the config file. Other feature will soon be handled in a similar way.
79 lines
3.3 KiB
Python
79 lines
3.3 KiB
Python
import pytest
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import unittest
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import warnings
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import math
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import server.test.decode_fbs as decode_fbs
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from server.data_anndata.anndata_adaptor import AnndataAdaptor
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from server.common.errors import FilterError
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from server.common.data_locator import DataLocator
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from server.common.app_config import AppConfig
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class NaNTest(unittest.TestCase):
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def setUp(self):
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self.args = {
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"embeddings__names": ["umap"],
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"presentation__max_categories": 100,
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"single_dataset__obs_names": None,
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"single_dataset__var_names": None,
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"diffexp__lfc_cutoff": 0.01,
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}
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config = AppConfig()
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config.update(**self.args)
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locator = DataLocator("test/test_datasets/nan.h5ad")
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config.update(single_dataset__datapath=locator.path)
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config.complete_config()
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", category=UserWarning)
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self.data = AnndataAdaptor(locator, config)
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self.data._create_schema()
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def test_load(self):
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with self.assertWarns(UserWarning):
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config = AppConfig()
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config.update(**self.args)
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locator = DataLocator("test/test_datasets/nan.h5ad")
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config.update(single_dataset__datapath=locator.path)
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config.complete_config()
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self.data = AnndataAdaptor(locator, config)
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def test_init(self):
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self.assertEqual(self.data.cell_count, 100)
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self.assertEqual(self.data.gene_count, 100)
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epsilon = 0.000_005
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self.assertTrue(self.data.data.X[0, 0] - -0.171_469_51 < epsilon)
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def test_dataframe(self):
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data_frame_var = decode_fbs.decode_matrix_FBS(self.data.data_frame_to_fbs_matrix(None, "var"))
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self.assertIsNotNone(data_frame_var)
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self.assertEqual(data_frame_var["n_rows"], 100)
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self.assertEqual(data_frame_var["n_cols"], 100)
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self.assertTrue(math.isnan(data_frame_var["columns"][3][3]))
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with pytest.raises(FilterError):
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self.data.data_frame_to_fbs_matrix("an erroneous filter", "var")
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with pytest.raises(FilterError):
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filter_ = {"filter": {"obs": {"index": [1, 99, [200, 300]]}}}
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self.data.data_frame_to_fbs_matrix(filter_["filter"], "var")
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def test_dataframe_obs_not_implemented(self):
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with self.assertRaises(ValueError) as cm:
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decode_fbs.decode_matrix_FBS(self.data.data_frame_to_fbs_matrix(None, "obs"))
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self.assertIsNotNone(cm.exception)
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def test_annotation(self):
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annotations = decode_fbs.decode_matrix_FBS(self.data.annotation_to_fbs_matrix("obs"))
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obs_index_col_name = self.data.schema["annotations"]["obs"]["index"]
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self.assertEqual(annotations["col_idx"], [obs_index_col_name, "n_genes", "percent_mito", "n_counts", "louvain"])
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self.assertEqual(annotations["n_rows"], 100)
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self.assertTrue(math.isnan(annotations["columns"][2][0]))
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annotations = decode_fbs.decode_matrix_FBS(self.data.annotation_to_fbs_matrix("var"))
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var_index_col_name = self.data.schema["annotations"]["var"]["index"]
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self.assertEqual(annotations["col_idx"], [var_index_col_name, "n_cells", "var_with_nans"])
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self.assertEqual(annotations["n_rows"], 100)
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self.assertTrue(math.isnan(annotations["columns"][2][0]))
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